Maja Rudolph

Affiliations:
  • Bosch Center for AI, Pittsburgh, PA, USA


According to our database1, Maja Rudolph authored at least 33 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
Anomaly Detection of Tabular Data Using LLMs.
CoRR, 2024

Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior.
CoRR, 2024

On the Challenges and Opportunities in Generative AI.
CoRR, 2024

Towards Fast Stochastic Sampling in Diffusion Generative Models.
CoRR, 2024

Hybrid Modeling Design Patterns.
CoRR, 2024

Efficient Integrators for Diffusion Generative Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection.
Trans. Mach. Learn. Res., 2023

Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data.
CoRR, 2023

LoRA ensembles for large language model fine-tuning.
CoRR, 2023

Deep Anomaly Detection on Tennessee Eastman Process Data.
CoRR, 2023

Zero-Shot Anomaly Detection without Foundation Models.
CoRR, 2023

Zero-Shot Anomaly Detection via Batch Normalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Anomaly Detection under Labeling Budget Constraints.
Proceedings of the International Conference on Machine Learning, 2023

2022
Complex-Valued Autoencoders for Object Discovery.
Trans. Mach. Learn. Res., 2022

Detecting Anomalies within Time Series using Local Neural Transformations.
CoRR, 2022

Raising the Bar in Graph-level Anomaly Detection.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Modeling Irregular Time Series with Continuous Recurrent Units.
Proceedings of the International Conference on Machine Learning, 2022

Latent Outlier Exposure for Anomaly Detection with Contaminated Data.
Proceedings of the International Conference on Machine Learning, 2022

2021
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks.
Entropy, 2021

Switching Recurrent Kalman Networks.
CoRR, 2021

Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Neural Transformation Learning for Deep Anomaly Detection Beyond Images.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Variational Dynamic Mixtures.
CoRR, 2020

Deterministic Inference of Neural Stochastic Differential Equations.
CoRR, 2020

2019
Extending Machine Language Models toward Human-Level Language Understanding.
CoRR, 2019

2018
Exponential Family Embeddings.
PhD thesis, 2018

Dynamic Embeddings for Language Evolution.
Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

2017
Dynamic Bernoulli Embeddings for Language Evolution.
CoRR, 2017

Structured Embedding Models for Grouped Data.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Edward: A library for probabilistic modeling, inference, and criticism.
CoRR, 2016

A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance.
Proceedings of the 25th International Conference on World Wide Web, 2016

Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve.
Proceedings of the 25th International Conference on World Wide Web, 2016

Exponential Family Embeddings.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016


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